Commands and settings API#

A System Coupling analysis is defined in terms of a hierarchical data model of settings. An API is exposed that provides direct access to the data model and commands that assist in setting up and solving an analysis.

Although it is possible to set up an analysis by directly assigning the relevant data model objects and settings, the expected and more convenient approach is to use the commands provided to set up the main objects. You then use direct data model assignment to fine tune the setup.

The API implementation is built on a number of generic objects. Subsequent sections provide brief descriptions of these objects.

Relationship with the “native” System Coupling API#

The API exposed in PySystemCoupling is mainly an adaptation of the API that exists natively in Ansys System Coupling.

If you are already familiar with System Coupling, or you want to consult the System Coupling documentation for more in-depth advice on some aspect of the system, you should find it easy to translate to the API that is exposed in PySystemCoupling.

The key differences are as follows:

  • While names of commands and data model elements follow camel case conventions in System Coupling, the names of commands and data models in PySystemCoupling follow snake case convention, which is the preferred Pythonic naming convention. Thus, the AddParticipant command in System Coupling becomes the add_participant command in PySystemCoupling. Similarly, the CouplingInterface data model object in System Coupling becomes the coupling_interface data model object in PySystemCoupling.

  • Commands and queries in System Coupling are all exposed in its Python environment as top-level global names. Commands in PySystemCoupling are exposed as callable objects that are accessible as attributes of one of the root attributes of the Session class: case, setup, and case.

  • In System Coupling, you manipulate and query the setup data model in one of these ways:

    • Use high level commands to create and initialize the main objects in the data model. For example, use the AddParticipant command to add a new coupling participant and the AddDataTransfer command to add a new data transfer object.

    • Access individual settings using a path-like syntax, starting at a root object returned by the DatamodelRoot command. For example:

      # Get the root object
      root = DatamodelRoot()
      # Path-like attribute access to navigate to a setting and assign a value to it
      ].DataTransfer["Force"].Value = "force*scaleFactor"
      # Similarly, path-like attribute access to query setting value
  • In PySystemCoupling, you manipulate and query the setup data model in essentially the same way. Setup commands are exposed from the setup attribute of the Session object. This attribute also plays the role of DatamodelRoot() in the preceding code. It serves as an entry point to accessing individual settings via a similar path-like syntax:

    # Given a Session object, get the setup root
    setup = session.setup
    # Assign a setting using "path" syntax
    ].data_transfer["Force"].value = "force*scaleFactor"
    # Query a setting

Direct access to the native System Coupling API#

If you want to try to quickly translate an existing System Coupling script to the PySystemCoupling environment, or you need to access a feature not currently exposed in PySystemCoupling, you can use the _native_api attribute that the Session class offers as a back door for directly accessing the native System Coupling API. However, you must still make some adjustments to the existing System Coupling script because calls must be via the _native_api attribute rather than via global commands.

For example, here is a command in a System Coupling script:


When using the native API in PySystemCoupling, the equivalent would be:

# Given a Session object, get the native api
api = session._native_api


You should not use the _native_api attribute generally, but you might find using it necessary in specific and limited circumstances.

Top-level objects#

The commands and settings API is accessible via the top-level attributes of the Session class: case, setup, and solution. These top-level attributes are all instances of the Container type. For links to commands for these root attributes, see Settings API content.

>>> import ansys.systemcoupling.core as pysystemcoupling
>>> analysis = pysystemcoupling.launch()
>>> setup = analysis.setup

Container object types#

In essence, the data model settings that define a System Coupling analysis consist of a collection of primitive property values, such as Integer, Real, String, and Boolean. To provide a structure to the settings, they are organized as groups of properties in container objects.

There are two types of container objects: Container and NamedContainer.

A Container object represents a grouping of primitive settings, accessible as property attributes. A Container object can also contain statically defined child objects, which are accessible as attributes. For example, setup.output_control.results refers to the results child of the output_control child of the setup object. The names of child objects can be accessed with the child_names attribute of the Container object.

The names of the primitive settings attributes of a Container can be obtained via its property_names_types attribute. Although, this is mainly for internal use, its value is a list of tuples, the first element of each tuple being the name of a primitive setting of the Container. As an example of such a setting, the option setting of the output_control object is accessed as setup.output_control.option. This particular setting is a string value.

A NamedContainer object is a container holding dynamically created named objects of its specified child type, which is accessible via child_object_type attribute. Because a specific named object can be accessed using the [] index operator, a NamedContainer object behaves somewhat like a Python dictionary. For example, setup.coupling_interface['intf-1'] refers to the coupling_interface object with name intf-1. You can use the get_object_names() method in the container class to obtain a list of the names of objects held by the NamedContainer. In practice, the named object instances are Container objects. Thus, in the example just given, setup.coupling_interface['intf-1'] is a Container object.

Container states#

You access the state of any container object by calling it. The call returns the state of its properties and any children as a nested dictionary. The call syntax is an alias for the get_state method, which you can use as a more explicit alternative.

You modify the state of a container by assigning the corresponding attribute in its parent object. This assignment can be done at any level. The assigned state value should be a dictionary. Again, a more explicit alternative is available in the form of the set_state method.

You query and assign individual settings as properties on their container objects.

You use the print_state method to print the current state of the container in a simple text format.


Commands are methods of settings objects that you use to modify the state of the app. The command_names attribute of a settings object provides the names of its commands.

You can pass keyword arguments to commands if needed. You use the arguments attribute to access the list of valid arguments. If an argument is not specified and is optional, its default value is used. Arguments are also settings objects and can be either a primitive type or container type.


The implementation of the settings classes is sufficiently flexible to allow commands to be exposed at any level of the container hierarchy. This is not exploited in the current API, but there is scope to do so in future extensions of the API. For example, where a command currently takes a reference to a data model object as an argument, allowing the command to be called on the relevant object could avoid the explicit argument.

Settings API content#

For information on commands related to settings, see: